The Internet and media enabled portable computing devices have dramatically altered the processes for generating and consuming media content. Presently, users can consume media content virtually anywhere at any time, as long as they have access to a media capable device with an Internet connection. The convenience of being able to view media content via the Internet, essentially on demand, has resulted in explosive growth of Internet media consumption. Internet media traffic is currently approaching a majority of consumer Internet traffic, and the rate of demand is projected to continue increasing.
The sheer quantity of media content available to users can make targeting relevant related content for consumption challenging. Millions of people around the world have the capability to produce media content, and popular online services can receive tens of hours worth of newly uploaded user-generated content every minute. In addition, traditional media outlets now have the ability to enable consumers to access archives containing large amounts of older media content, along with newly generated content. Due in part to the large amounts of media content available, online services may have difficulty selecting relevant media content related to content consumed by a user.
A technique that has been commonly employed by online services to determine related content includes identifying related content based on attributes of previously consumed media content. However, different attributes may have varying degrees of usefulness for identifying related content based on user context. For instance, a user consuming media content in a first context may find related content based on a set of shared attributes very useful; however, another user consuming the media content in a different context may not find the same related content desirable or relevant.